Evaluating the performance of Simultaneous Localization and Mapping (SLAM) algorithms is essential for scientists and users of robotic systems alike. But there are a multitude of different permutations of possible options of hardware setups and algorithm configurations, as well as different datasets and algorithms, such that it was previously infeasible to thoroughly compare SLAM systems against the full state of the art. To solve that we present the SLAM Hive Benchmarking Suite, which is able to analyze SLAM algorithms in 1000's of mapping runs, through its utilization of container technology and deployment in the cloud. This paper presents the architecture and open source implementation of SLAM Hive and compares it to existing efforts on SLAM evaluation. We perform mapping runs with popular visual, RGBD and LiDAR based SLAM algorithms against commonly used datasets and show how SLAM Hive can be used to conveniently analyze the results against various aspects. Through this we envision that SLAM Hive can become an essential tool for proper comparisons and evaluations of SLAM algorithms and thus drive the scientific development in the research on SLAM. The open source software as well as a demo to show the live analysis of 1000's of mapping runs can be found on our SLAM Hive website.

翻译：评估同步定位与建图（SLAM）算法的性能对科研人员和机器人系统用户都至关重要。然而，硬件配置与算法参数存在大量可能的组合变体，加之不同的数据集和算法（如视觉、RGBD和激光雷达SLAM），以往难以在完整的技术前沿范围内对SLAM系统进行彻底比较。为此，我们提出了SLAM Hive基准测试套件，该套件通过容器化技术与云端部署，能够对SLAM算法进行数千次建图运行的规模化分析。本文介绍了SLAM Hive的架构设计与开源实现，并与现有SLAM评估方案进行对比。我们使用主流的视觉、RGBD及激光雷达SLAM算法在常用数据集上执行建图任务，并展示如何利用SLAM Hive便捷地从多维度分析结果。我们期望SLAM Hive能成为SLAM算法科学比较与评估的重要工具，从而推动SLAM研究领域的学术发展。开源软件及可实时分析数千次建图运行的演示系统均发布于SLAM Hive官方网站。